docstr: Base container abstract class allowing to mirror the Sklearn API.
  *SklearnContainer* enables the use of `predict`, `predict_proba` etc. API of Sklearn
  also over the models generated by Hummingbird (irrespective of the selected backend).
  
  Args:
      model: Any Hummingbird supported model
      n_threads: How many threads should be used by the containter to run the model. None means use all threads.
      batch_size: If different than None, split the input into batch_size partitions and score one partition at a time.
      extra_config: Some additional configuration parameter.
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code: def __init__(
      self, model, n_threads=None, batch_size=None, extra_config={}
  ):
      """
      Base container abstract class allowing to mirror the Sklearn API.
      *SklearnContainer* enables the use of `predict`, `predict_proba` etc. API of Sklearn
      also over the models generated by Hummingbird (irrespective of the selected backend).
  
      Args:
          model: Any Hummingbird supported model
          n_threads: How many threads should be used by the containter to run the model. None means use all threads.
          batch_size: If different than None, split the input into batch_size partitions and score one partition at a time.
          extra_config: Some additional configuration parameter.
      """
      self._model = model
      self._n_threads = n_threads
      self._extra_config = extra_config
      self._batch_size = batch_size
      self._check_dataframe_to_array = (
          constants.TEST_INPUT not in extra_config
      )
  
imports: from abc import ABC, abstractmethod
  from hummingbird.ml._utils import pandas_installed
  from hummingbird.ml.operator_converters import constants
label_desc: Use image node_id 1 to create a new SklearnContainer object from inherited base classes: ABC with example: obj = SklearnContainer(model, n_threads, batch_size, extra_config)